code: def _partition_estimators(n_estimators, n_jobs, verbose=False):
      """Private function used to partition estimators between jobs."""
      # Compute the number of jobs
      n_jobs = min(effective_n_jobs(n_jobs), n_estimators)
  
      # Partition estimators between jobs
      n_estimators_per_job = np.full(
          n_jobs, n_estimators // n_jobs, dtype=int
      )
      n_estimators_per_job[: n_estimators % n_jobs] += 1
      starts = np.cumsum(n_estimators_per_job)
  
      xdiff = [starts[n] - starts[n - 1] for n in range(1, len(starts))]
  
      if verbose:
          print("Split among workers default:", starts, xdiff)
      return (
          n_estimators_per_job.tolist(),
          [0] + starts.tolist(),
          n_jobs,
      )
  
file_path: SUOD/suod/models/parallel_processes.py
imports: from sklearn.base import clone
  import numpy
  from scipy.stats import rankdata
  from joblib import effective_n_jobs
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from pyod.utils.utility import score_to_label
  from .jl_projection import jl_fit_transform, jl_transform
  from ..utils.utility import raw_score_to_proba
label_desc: Use image node_id 3 for calling a global function with example usage: _partition_estimators(n_estimators, n_jobs, verbose) and returns: n_estimators_per_job,  unknown,  n_jobs